The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal r...The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.展开更多
Climate change impact studies on hydrologic regime have been until recently restricted mainly because of the coarse spatial and temporal resolution of the Global Circulation Models(GCMs)outputs. Nevertheless,local met...Climate change impact studies on hydrologic regime have been until recently restricted mainly because of the coarse spatial and temporal resolution of the Global Circulation Models(GCMs)outputs. Nevertheless,local meteorological variables can be derived from GCMs scenarios using downscaling techniques.In the present study,the Statistical DownScaling Model(SDSM)was selected for展开更多
基金Supported by the National Key R&D Plan(2018YFC1506500)Open Research Fund Project of Key Laboratory of Ecological Environment Meteorology of Qinling Mountains and Loess Plateau of Shaanxi Provincial Meteorological Bureau(2020Y-13)+1 种基金Open Research Fund of Shangluo Key Laboratory of Climate Adaptable City(SLSYS2022007)Shangluo Demonstration Project of Qinling Ecological Monitoring Service System(2020-611002-74-01-006200)。
文摘The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.
文摘Climate change impact studies on hydrologic regime have been until recently restricted mainly because of the coarse spatial and temporal resolution of the Global Circulation Models(GCMs)outputs. Nevertheless,local meteorological variables can be derived from GCMs scenarios using downscaling techniques.In the present study,the Statistical DownScaling Model(SDSM)was selected for
